Learning in Autonomous Transportation Systems

Interdisciplinary Areas: Data and Engineering Applications, Autonomous and Connected Systems, Smart City, Infrastructure, Transportation

Project Description

This project is at the intersection of machine learning, network science and intelligent transportation. The concepts of mean field control, decentralizable algorithms, network science and reinforcement learning will be used for problems in scheduling of vehicles, traffic signal control, congestion pricing, management of autonomous and human vehicles, etc. The work will be a combination of theoretical contributions in machine learning, algorithm development and testing in real-world intelligent transportation systems.

Start Date


Postdoc Qualifications

Desired candidate would have PhD in ECE, CS, Stats, or related areas, with experience with both mathematical proofs as well as programming in machine learning. Top-tier papers related to machine learning will be preferred, and some experience in applications to intelligent transportation will be a big plus.


Satish Ukkusuri, CE
Vaneet Aggarwal, IE (ECE, by courtesy)
Chen, Chacha, et al. "Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 04. 2020.
Chen, Jiayu, et al. "DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery." Proceedings of the International Conference on Automated Planning and Scheduling. Vol. 31. 2021.
Gu, Haotian, et al. "Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis." arXiv preprint arXiv:2002.04131 (2020).
Pu, Yuan, et al. "Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning." arXiv preprint arXiv:2104.06655 (2021).
Li, Li, Victor Okoth, and Saif Eddin Jabari. "Backpressure control with estimated queue lengths for urban network traffic." arXiv preprint arXiv:2006.15549 (2020).
Zhan, X., Li, R., & Ukkusuri, S. V. (2020). Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data. Transportation Research Part C: Emerging Technologies, 117, 102660.